{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 端到端机器学习项目（预测房价）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 加载数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "HOUSING_PATH = 'DataSet/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def load_housing_data(housing_path=HOUSING_PATH):\n",
    "    csv_path = os.path.join(housing_path, \"housing.csv\")\n",
    "    return pd.read_csv(csv_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 快速查看数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \n",
       "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
       "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
       "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
       "4       565.0       259.0         3.8462            342200.0        NEAR BAY  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing = load_housing_data()\n",
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20640 entries, 0 to 20639\n",
      "Data columns (total 10 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   longitude           20640 non-null  float64\n",
      " 1   latitude            20640 non-null  float64\n",
      " 2   housing_median_age  20640 non-null  float64\n",
      " 3   total_rooms         20640 non-null  float64\n",
      " 4   total_bedrooms      20433 non-null  float64\n",
      " 5   population          20640 non-null  float64\n",
      " 6   households          20640 non-null  float64\n",
      " 7   median_income       20640 non-null  float64\n",
      " 8   median_house_value  20640 non-null  float64\n",
      " 9   ocean_proximity     20640 non-null  object \n",
      "dtypes: float64(9), object(1)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "housing.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20433.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-119.569704</td>\n",
       "      <td>35.631861</td>\n",
       "      <td>28.639486</td>\n",
       "      <td>2635.763081</td>\n",
       "      <td>537.870553</td>\n",
       "      <td>1425.476744</td>\n",
       "      <td>499.539680</td>\n",
       "      <td>3.870671</td>\n",
       "      <td>206855.816909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.003532</td>\n",
       "      <td>2.135952</td>\n",
       "      <td>12.585558</td>\n",
       "      <td>2181.615252</td>\n",
       "      <td>421.385070</td>\n",
       "      <td>1132.462122</td>\n",
       "      <td>382.329753</td>\n",
       "      <td>1.899822</td>\n",
       "      <td>115395.615874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-124.350000</td>\n",
       "      <td>32.540000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.499900</td>\n",
       "      <td>14999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-121.800000</td>\n",
       "      <td>33.930000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1447.750000</td>\n",
       "      <td>296.000000</td>\n",
       "      <td>787.000000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>2.563400</td>\n",
       "      <td>119600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-118.490000</td>\n",
       "      <td>34.260000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>2127.000000</td>\n",
       "      <td>435.000000</td>\n",
       "      <td>1166.000000</td>\n",
       "      <td>409.000000</td>\n",
       "      <td>3.534800</td>\n",
       "      <td>179700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-118.010000</td>\n",
       "      <td>37.710000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>3148.000000</td>\n",
       "      <td>647.000000</td>\n",
       "      <td>1725.000000</td>\n",
       "      <td>605.000000</td>\n",
       "      <td>4.743250</td>\n",
       "      <td>264725.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>-114.310000</td>\n",
       "      <td>41.950000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>39320.000000</td>\n",
       "      <td>6445.000000</td>\n",
       "      <td>35682.000000</td>\n",
       "      <td>6082.000000</td>\n",
       "      <td>15.000100</td>\n",
       "      <td>500001.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          longitude      latitude  housing_median_age   total_rooms  \\\n",
       "count  20640.000000  20640.000000        20640.000000  20640.000000   \n",
       "mean    -119.569704     35.631861           28.639486   2635.763081   \n",
       "std        2.003532      2.135952           12.585558   2181.615252   \n",
       "min     -124.350000     32.540000            1.000000      2.000000   \n",
       "25%     -121.800000     33.930000           18.000000   1447.750000   \n",
       "50%     -118.490000     34.260000           29.000000   2127.000000   \n",
       "75%     -118.010000     37.710000           37.000000   3148.000000   \n",
       "max     -114.310000     41.950000           52.000000  39320.000000   \n",
       "\n",
       "       total_bedrooms    population    households  median_income  \\\n",
       "count    20433.000000  20640.000000  20640.000000   20640.000000   \n",
       "mean       537.870553   1425.476744    499.539680       3.870671   \n",
       "std        421.385070   1132.462122    382.329753       1.899822   \n",
       "min          1.000000      3.000000      1.000000       0.499900   \n",
       "25%        296.000000    787.000000    280.000000       2.563400   \n",
       "50%        435.000000   1166.000000    409.000000       3.534800   \n",
       "75%        647.000000   1725.000000    605.000000       4.743250   \n",
       "max       6445.000000  35682.000000   6082.000000      15.000100   \n",
       "\n",
       "       median_house_value  \n",
       "count        20640.000000  \n",
       "mean        206855.816909  \n",
       "std         115395.615874  \n",
       "min          14999.000000  \n",
       "25%         119600.000000  \n",
       "50%         179700.000000  \n",
       "75%         264725.000000  \n",
       "max         500001.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 查看每个类别当中的属性"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "unique()枚举属性值；value_counts()枚举属性值与对应特征计数；"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['NEAR BAY', '<1H OCEAN', 'INLAND', 'NEAR OCEAN', 'ISLAND'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing[\"ocean_proximity\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<1H OCEAN     9136\n",
       "INLAND        6551\n",
       "NEAR OCEAN    2658\n",
       "NEAR BAY      2290\n",
       "ISLAND           5\n",
       "Name: ocean_proximity, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing[\"ocean_proximity\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "画出属性的直方图进行观察"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "housing.hist(bins=50, figsize=(20,15))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 创建测试集"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果你查看了测试集，就会不经意地按照测试集中的规律来选择某个特定的机器学习模型。再当你使用测试集来评估误差率时，就会导致评估过于乐观，而实际部署的系统表现就会差。这称为数据透视偏差。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import hashlib\n",
    "\n",
    "def test_set_check(identifier, test_ratio, hash):\n",
    "    return hash(np.int64(identifier)).digest()[-1] < 256 * test_ratio\n",
    "\n",
    "def split_train_test_by_id(data, test_ratio, id_column, hash=hashlib.md5):\n",
    "    ids = data[id_column]\n",
    "    in_test_set = ids.apply(lambda id_: test_set_check(id_, test_ratio, hash))\n",
    "    return data.loc[~in_test_set], data.loc[in_test_set]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "方法一：使用房地产数据集的行索引作为ID，使用hash的方法进行训练集与测试集的分类；需保证新数据均放到现有数据尾部，且没有行被删除；"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'housing_with_id' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-11-8542d18f92e7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mhousing_with_id\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"id\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhousing\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"longitude\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m*\u001b[0m \u001b[1;36m1000\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mhousing\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"latitude\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mtrain_set\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest_set\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msplit_train_test_by_id\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhousing_with_id\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0.2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"id\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'housing_with_id' is not defined"
     ]
    }
   ],
   "source": [
    "housing_with_id[\"id\"] = housing[\"longitude\"] * 1000 + housing[\"latitude\"]\n",
    "train_set, test_set = split_train_test_by_id(housing_with_id, 0.2, \"id\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "方法二：可使用最稳定特征来创建唯一识别码；如经纬度恒定不变可见时间尺度内，因而可将两者结合，形成一个ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_with_id = housing.reset_index()   # adds an `index` column\n",
    "train_set, test_set = split_train_test_by_id(housing_with_id, 0.2, \"index\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "方法三：Scikit-Learn 提供了一些函数，可以用多种方式将数据集分割成多个子集。最简单的函数是train_test_split，它的作用和之前的函数split_train_test很像，并带有其它一些功能。<br>\n",
    "首先，它有一个random_state参数，可以设定前面讲过的随机生成器种子；<br>\n",
    "第二，你可以将种子传递给多个行数相同的数据集，可以在相同的索引上分割数据集（这个功能非常有用，比如你的标签值是放在另一个DataFrame里的）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "train_set,test_set = train_test_split(housing,test_size = 0.2,random_state = 42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 连续属性离散化"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "假设专家告诉你，收入中位数是预测房价中位数非常重要的属性。你可能想要保证测试集可以代表整体数据集中的多种收入分类。因为收入中位数是一个连续的数值属性，你首先需要创建一个收入类别属性。再仔细地看一下收入中位数的柱状图（译注：该图是对收入中位数处理过后的图）："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这意味着，你不能有过多的分层，且每个分层都要足够大。后面的代码通过将收入中位数除以 1.5（以限制收入分类的数量），创建了一个收入类别属性，用ceil对值舍入（以产生离散的分类），然后将所有大于 5 的分类归入到分类 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing[\"income_cat\"] = np.ceil(housing[\"median_income\"] / 1.5)\n",
    "housing[\"income_cat\"].where(housing[\"income_cat\"] < 5, 5.0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing[\"income_cat\"].hist(bins=50, figsize=(8,8))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用Scikit-Learn的StratifiedShufflesplit进行分层抽样"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "\n",
    "split = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42)\n",
    "\n",
    "for train_index, test_index in split.split(housing, housing[\"income_cat\"]):\n",
    "    strat_train_set = housing.loc[train_index]\n",
    "    strat_test_set = housing.loc[test_index]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "检查下结果是否符合预期。可以在完整的房产数据集中查看收入分类比例："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0    0.350581\n",
       "2.0    0.318847\n",
       "4.0    0.176308\n",
       "5.0    0.114438\n",
       "1.0    0.039826\n",
       "Name: income_cat, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing[\"income_cat\"].value_counts() / len(housing)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "删除\"income-cat\"属性，使数据回到初始状态"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "for set in (strat_train_set, strat_test_set):\n",
    "    set.drop([\"income_cat\"], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据探索和可视化、发现规律"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "创建副本，避免损伤训练集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing = strat_train_set.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 地理数据可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2515237d460>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.scatterplot(x = \"longitude\", y =\"latitude\", data = housing)\n",
    "# housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "将alpha设置为0.1更容易看出数据点的密度；"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2515200ce50>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x = \"longitude\", y =\"latitude\", data = housing, alpha=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在来看房价（图 2-13）。每个圈的半径表示街区的人口（选项s），颜色代表价格（选项c）。我们用预先定义的名为jet的颜色图（选项cmap），它的范围是从蓝色（低价）到红色（高价）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'matplotlib.pyplot' has no attribute 'colorar'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-22-a7093dd873b4>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      5\u001b[0m sns.scatterplot(x = \"longitude\", y =\"latitude\", data = housing, alpha=0.4,\n\u001b[0;32m      6\u001b[0m                 size=housing[\"population\"]/100,hue = housing[\"population\"]/100,legend =False,palette=\"Set2\")\n\u001b[1;32m----> 7\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolorar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msns\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      8\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlegend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mloc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'best'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: module 'matplotlib.pyplot' has no attribute 'colorar'"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", alpha=0.4,\n",
    "#     s=housing[\"population\"]/100, label=\"population\",\n",
    "#     c=\"median_house_value\", cmap=plt.get_cmap(\"jet\"), colorbar=True,\n",
    "# )\n",
    "sns.scatterplot(x = \"longitude\", y =\"latitude\", data = housing, alpha=0.4,\n",
    "                size=housing[\"population\"]/100,hue = housing[\"population\"]/100,legend =False,palette=\"Set2\")\n",
    "plt.colorar(sns)\n",
    "plt.legend(loc = 'best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x25152378280>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", alpha=0.4,\n",
    "    s=housing[\"population\"]/100, label=\"population\",\n",
    "    c=\"median_house_value\", cmap=plt.get_cmap(\"YlGn\"), colorbar=True,\n",
    ")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 查找关联"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用corr()方法计算出每对属性间的标准相关系数（standard correlation coefficient，也称作皮尔逊相关系数）："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_matrix = housing.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在来看下每个属性和房价中位数的关联度："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_matrix[\"median_house_value\"].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.heatmap(housing.corr(),cmap='YlGn')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "另一种检测属性间相关系数的方法是使用 Pandas 的scatter_matrix函数，它能画出每个数值属性对每个其它数值属性的图。因为现在共有 11 个数值属性，你可以得到11 ** 2 = 121张图，在一页上画不下，所以只关注几个和房价中位数最有可能相关的属性："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = ['median_house_value','median_income','total_rooms','housing_median_age']\n",
    "# pd.plotting.scatter_matrix(housing[features],figsize = (8,8))\n",
    "sns.pairplot(housing[features])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "最有希望用来预测房价中位数的属性是收入中位数，因此将这张图放大"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# housing.plot(kind=\"scatter\", x=\"median_income\",y=\"median_house_value\",\n",
    "#              alpha=0.1)\n",
    "sns.scatterplot(x = \"median_income\", y =\"median_house_value\", data = housing, alpha=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这张图说明了几点。首先，相关性非常高；可以清晰地看到向上的趋势，并且数据点不是非常分散。第二，我们之前看到的最高价，清晰地呈现为一条位于 500000 美元的水平线。这张图也呈现了一些不是那么明显的直线：一条位于 450000 美元的直线，一条位于 350000 美元的直线，一条在 280000 美元的线，和一些更靠下的线。你可能希望去除对应的街区，以防止算法重复这些巧合;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 属性组合试验"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "给算法准备数据之前，你需要做的最后一件事是尝试多种属性组合。例如，如果你不知道某个街区有多少户，该街区的总房间数就没什么用。你真正需要的是每户有几个房间。相似的，总卧室数也不重要：你可能需要将其与房间数进行比较。每户的人口数也是一个有趣的属性组合。让我们来创建这些新的属性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing[\"rooms_per_household\"] = housing[\"total_rooms\"]/housing[\"households\"]\n",
    "housing[\"bedrooms_per_room\"] = housing[\"total_bedrooms\"]/housing[\"total_rooms\"]\n",
    "housing[\"population_per_household\"]=housing[\"population\"]/housing[\"households\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在，再来看相关矩阵："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_matrix = housing.corr()\n",
    "corr_matrix[\"median_house_value\"].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "看起来不错！与总房间数或卧室数相比，新的bedrooms_per_room属性与房价中位数的关联更强。显然，卧室数/总房间数的比例越低，房价就越高。每户的房间数也比街区的总房间数的更有信息，很明显，房屋越大，房价就越高。<bar>\n",
    "\n",
    "这一步的数据探索不必非常完备，此处的目的是有一个正确的开始，快速发现规律，以得到一个合理的原型。但是这是一个交互过程：一旦你得到了一个原型，并运行起来，你就可以分析它的输出，进而发现更多的规律，然后再回到数据探索这步。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 为机器学习算法准备数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "但是，还是先回到干净的训练集（通过再次复制strat_train_set），将预测量和标签分开，因为我们不想对预测量和目标值应用相同的转换（注意drop()创建了一份数据的备份，而不影响strat_train_set）："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_lables = strat_train_set[\"median_house_value\"].copy()\n",
    "housing = strat_train_set.drop(\"median_house_value\",axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据清洗"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "大多机器学习算法不能处理缺失的特征，因此先创建一些函数来处理特征缺失的问题。前面，你应该注意到了属性total_bedrooms有一些缺失值。有三个解决选项：\n",
    "\n",
    "选项1：去掉对应的街区；\n",
    "\n",
    "选项2：去掉整个属性；\n",
    "\n",
    "选项3：进行赋值（0、平均值、中位数等等）。\n",
    "\n",
    "用DataFrame的dropna()，drop()，和fillna()方法，可以方便地实现："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing.dropna(subset=[\"total_bedrooms\"])    # 选项 1\n",
    "housing.drop(\"total_bedrooms\", axis=1)       # 选项 2\n",
    "median = housing[\"total_bedrooms\"].median()\n",
    "housing[\"total_bedrooms\"].fillna(median)     # 选项 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果选择选项 3，你需要计算训练集的中位数，用中位数填充训练集的缺失值，不要忘记保存该中位数。后面用测试集评估系统时，需要替换测试集中的缺失值，也可以用来实时替换新数据中的缺失值。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scikit-Learn 提供了一个方便的类来处理缺失值：Imputer。下面是其使用方法：首先，需要创建一个Imputer实例，指定用某属性的中位数来替换该属性所有的缺失值："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "imputer = SimpleImputer(strategy=\"median\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "因为只有数值属性才能算出中位数，我们需要创建一份不包括文本属性ocean_proximity的数据副本"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_num = housing.drop(\"ocean_proximity\", axis=1)\n",
    "imputer.fit(housing_num)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "imputer计算出了每个属性的中位数，并将结果保存在了实例变量statistics_中。虽然此时只有属性total_bedrooms存在缺失值，但我们不能确定在以后的新的数据中会不会有其他属性也存在缺失值，所以安全的做法是将imputer应用到每个数值："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "imputer.statistics_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_num.median().values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在，你就可以使用这个“训练过的”imputer来对训练集进行转换，将缺失值替换为中位数："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = imputer.transform(housing_num)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "结果是一个包含转换后特征的普通的 Numpy 数组。如果你想将其放回到 PandasDataFrame中，也很简单："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_tr = pd.DataFrame(X, columns=housing_num.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "前面，我们丢弃了类别属性ocean_proximity，因为它是一个文本属性，不能计算出中位数。大多数机器学习算法更喜欢和数字打交道，所以让我们把这些文本标签转换为数字。\n",
    "\n",
    "在原书中使用LabelEncoder转换器来转换文本特征列的方式是错误的，该转换器只能用来转换标签（正如其名）。在这里使用LabelEncoder没有出错的原因是该数据只有一列文本特征值，在有多个文本特征列的时候就会出错。应使用factorize()方法来进行操作："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.preprocessing import LabelEncoder\n",
    "# encoder = LabelEncoder()\n",
    "housing_cat = housing['ocean_proximity']\n",
    "housing_cat_encoded,housing_categories = housing_cat.factorize()\n",
    "housing_cat_encoded[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "好了一些，现在就可以在任何 ML 算法里用这个数值数据了。你可以查看映射表，编码器是通过属性classes_来学习的（<1H OCEAN被映射为 0，INLAND被映射为 1，等等）："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(encoder.classes_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这种做法的问题是，ML 算法会认为两个临近的值比两个疏远的值要更相似。显然这样不对（比如，分类 0 和分类 4 就比分类 0 和分类 1 更相似）。要解决这个问题，一个常见的方法是给每个分类创建一个二元属性：当分类是<1H OCEAN，该属性为 1（否则为 0），当分类是INLAND，另一个属性等于 1（否则为 0），以此类推。这称作独热编码（One-Hot Encoding），因为只有一个属性会等于 1（热），其余会是 0（冷）。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scikit-Learn 提供了一个编码器OneHotEncoder，用于将整数分类值转变为独热向量。注意fit_transform()用于 2D 数组，而housing_cat_encoded是一个 1D 数组，所以需要将其变形："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "enconder = OneHotEncoder()\n",
    "housing_cat_1hot = encoder.fit_transform(housing_cat.values.reshape(-1,1))\n",
    "housing_cat_1hot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "注意输出结果是一个 SciPy 稀疏矩阵，而不是 NumPy 数组。当类别属性有数千个分类时，这样非常有用。经过独热编码，我们得到了一个有数千列的矩阵，这个矩阵每行只有一个 1，其余都是 0。使用大量内存来存储这些 0 非常浪费，所以稀疏矩阵只存储非零元素的位置。你可以像一个 2D 数据那样进行使用，但是如果你真的想将其转变成一个（密集的）NumPy 数组，只需调用toarray()方法：\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_cat_1hot.toarray()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "注意默认返回的结果是一个密集 NumPy 数组。向构造器LabelBinarizer传递sparse_output=True，就可以得到一个稀疏矩阵。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用类LabelBinarizer，我们可以用一步执行这两个转换（从文本分类到整数分类，再从整数分类到独热向量）：<bar>\n",
    "\n",
    "在原书中使用LabelBinarizer的方式也是错误的，该类也应用于标签列的转换。正确做法是使用 sklearn 即将提供的CategoricalEncoder类。如果在你阅读此文时 sklearn 中尚未提供此类，用如下方式代替：（来自Pull Request #9151）\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Definition of the CategoricalEncoder class, copied from PR #9151.\n",
    "# Just run this cell, or copy it to your code, do not try to understand it (yet).\n",
    "\n",
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "from sklearn.utils import check_array\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from scipy import sparse\n",
    "\n",
    "class CategoricalEncoder(BaseEstimator, TransformerMixin):\n",
    "    \"\"\"Encode categorical features as a numeric array.\n",
    "    The input to this transformer should be a matrix of integers or strings,\n",
    "    denoting the values taken on by categorical (discrete) features.\n",
    "    The features can be encoded using a one-hot aka one-of-K scheme\n",
    "    (``encoding='onehot'``, the default) or converted to ordinal integers\n",
    "    (``encoding='ordinal'``).\n",
    "    This encoding is needed for feeding categorical data to many scikit-learn\n",
    "    estimators, notably linear models and SVMs with the standard kernels.\n",
    "    Read more in the :ref:`User Guide <preprocessing_categorical_features>`.\n",
    "    Parameters\n",
    "    ----------\n",
    "    encoding : str, 'onehot', 'onehot-dense' or 'ordinal'\n",
    "        The type of encoding to use (default is 'onehot'):\n",
    "        - 'onehot': encode the features using a one-hot aka one-of-K scheme\n",
    "          (or also called 'dummy' encoding). This creates a binary column for\n",
    "          each category and returns a sparse matrix.\n",
    "        - 'onehot-dense': the same as 'onehot' but returns a dense array\n",
    "          instead of a sparse matrix.\n",
    "        - 'ordinal': encode the features as ordinal integers. This results in\n",
    "          a single column of integers (0 to n_categories - 1) per feature.\n",
    "    categories : 'auto' or a list of lists/arrays of values.\n",
    "        Categories (unique values) per feature:\n",
    "        - 'auto' : Determine categories automatically from the training data.\n",
    "        - list : ``categories[i]`` holds the categories expected in the ith\n",
    "          column. The passed categories are sorted before encoding the data\n",
    "          (used categories can be found in the ``categories_`` attribute).\n",
    "    dtype : number type, default np.float64\n",
    "        Desired dtype of output.\n",
    "    handle_unknown : 'error' (default) or 'ignore'\n",
    "        Whether to raise an error or ignore if a unknown categorical feature is\n",
    "        present during transform (default is to raise). When this is parameter\n",
    "        is set to 'ignore' and an unknown category is encountered during\n",
    "        transform, the resulting one-hot encoded columns for this feature\n",
    "        will be all zeros.\n",
    "        Ignoring unknown categories is not supported for\n",
    "        ``encoding='ordinal'``.\n",
    "    Attributes\n",
    "    ----------\n",
    "    categories_ : list of arrays\n",
    "        The categories of each feature determined during fitting. When\n",
    "        categories were specified manually, this holds the sorted categories\n",
    "        (in order corresponding with output of `transform`).\n",
    "    Examples\n",
    "    --------\n",
    "    Given a dataset with three features and two samples, we let the encoder\n",
    "    find the maximum value per feature and transform the data to a binary\n",
    "    one-hot encoding.\n",
    "    >>> from sklearn.preprocessing import CategoricalEncoder\n",
    "    >>> enc = CategoricalEncoder(handle_unknown='ignore')\n",
    "    >>> enc.fit([[0, 0, 3], [1, 1, 0], [0, 2, 1], [1, 0, 2]])\n",
    "    ... # doctest: +ELLIPSIS\n",
    "    CategoricalEncoder(categories='auto', dtype=<... 'numpy.float64'>,\n",
    "              encoding='onehot', handle_unknown='ignore')\n",
    "    >>> enc.transform([[0, 1, 1], [1, 0, 4]]).toarray()\n",
    "    array([[ 1.,  0.,  0.,  1.,  0.,  0.,  1.,  0.,  0.],\n",
    "           [ 0.,  1.,  1.,  0.,  0.,  0.,  0.,  0.,  0.]])\n",
    "    See also\n",
    "    --------\n",
    "    sklearn.preprocessing.OneHotEncoder : performs a one-hot encoding of\n",
    "      integer ordinal features. The ``OneHotEncoder assumes`` that input\n",
    "      features take on values in the range ``[0, max(feature)]`` instead of\n",
    "      using the unique values.\n",
    "    sklearn.feature_extraction.DictVectorizer : performs a one-hot encoding of\n",
    "      dictionary items (also handles string-valued features).\n",
    "    sklearn.feature_extraction.FeatureHasher : performs an approximate one-hot\n",
    "      encoding of dictionary items or strings.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, encoding='onehot', categories='auto', dtype=np.float64,\n",
    "                 handle_unknown='error'):\n",
    "        self.encoding = encoding\n",
    "        self.categories = categories\n",
    "        self.dtype = dtype\n",
    "        self.handle_unknown = handle_unknown\n",
    "\n",
    "    def fit(self, X, y=None):\n",
    "        \"\"\"Fit the CategoricalEncoder to X.\n",
    "        Parameters\n",
    "        ----------\n",
    "        X : array-like, shape [n_samples, n_feature]\n",
    "            The data to determine the categories of each feature.\n",
    "        Returns\n",
    "        -------\n",
    "        self\n",
    "        \"\"\"\n",
    "\n",
    "        if self.encoding not in ['onehot', 'onehot-dense', 'ordinal']:\n",
    "            template = (\"encoding should be either 'onehot', 'onehot-dense' \"\n",
    "                        \"or 'ordinal', got %s\")\n",
    "            raise ValueError(template % self.handle_unknown)\n",
    "\n",
    "        if self.handle_unknown not in ['error', 'ignore']:\n",
    "            template = (\"handle_unknown should be either 'error' or \"\n",
    "                        \"'ignore', got %s\")\n",
    "            raise ValueError(template % self.handle_unknown)\n",
    "\n",
    "        if self.encoding == 'ordinal' and self.handle_unknown == 'ignore':\n",
    "            raise ValueError(\"handle_unknown='ignore' is not supported for\"\n",
    "                             \" encoding='ordinal'\")\n",
    "\n",
    "        X = check_array(X, dtype=np.object, accept_sparse='csc', copy=True)\n",
    "        n_samples, n_features = X.shape\n",
    "\n",
    "        self._label_encoders_ = [LabelEncoder() for _ in range(n_features)]\n",
    "\n",
    "        for i in range(n_features):\n",
    "            le = self._label_encoders_[i]\n",
    "            Xi = X[:, i]\n",
    "            if self.categories == 'auto':\n",
    "                le.fit(Xi)\n",
    "            else:\n",
    "                valid_mask = np.in1d(Xi, self.categories[i])\n",
    "                if not np.all(valid_mask):\n",
    "                    if self.handle_unknown == 'error':\n",
    "                        diff = np.unique(Xi[~valid_mask])\n",
    "                        msg = (\"Found unknown categories {0} in column {1}\"\n",
    "                               \" during fit\".format(diff, i))\n",
    "                        raise ValueError(msg)\n",
    "                le.classes_ = np.array(np.sort(self.categories[i]))\n",
    "\n",
    "        self.categories_ = [le.classes_ for le in self._label_encoders_]\n",
    "\n",
    "        return self\n",
    "\n",
    "    def transform(self, X):\n",
    "        \"\"\"Transform X using one-hot encoding.\n",
    "        Parameters\n",
    "        ----------\n",
    "        X : array-like, shape [n_samples, n_features]\n",
    "            The data to encode.\n",
    "        Returns\n",
    "        -------\n",
    "        X_out : sparse matrix or a 2-d array\n",
    "            Transformed input.\n",
    "        \"\"\"\n",
    "        X = check_array(X, accept_sparse='csc', dtype=np.object, copy=True)\n",
    "        n_samples, n_features = X.shape\n",
    "        X_int = np.zeros_like(X, dtype=np.int)\n",
    "        X_mask = np.ones_like(X, dtype=np.bool)\n",
    "\n",
    "        for i in range(n_features):\n",
    "            valid_mask = np.in1d(X[:, i], self.categories_[i])\n",
    "\n",
    "            if not np.all(valid_mask):\n",
    "                if self.handle_unknown == 'error':\n",
    "                    diff = np.unique(X[~valid_mask, i])\n",
    "                    msg = (\"Found unknown categories {0} in column {1}\"\n",
    "                           \" during transform\".format(diff, i))\n",
    "                    raise ValueError(msg)\n",
    "                else:\n",
    "                    # Set the problematic rows to an acceptable value and\n",
    "                    # continue `The rows are marked `X_mask` and will be\n",
    "                    # removed later.\n",
    "                    X_mask[:, i] = valid_mask\n",
    "                    X[:, i][~valid_mask] = self.categories_[i][0]\n",
    "            X_int[:, i] = self._label_encoders_[i].transform(X[:, i])\n",
    "\n",
    "        if self.encoding == 'ordinal':\n",
    "            return X_int.astype(self.dtype, copy=False)\n",
    "\n",
    "        mask = X_mask.ravel()\n",
    "        n_values = [cats.shape[0] for cats in self.categories_]\n",
    "        n_values = np.array([0] + n_values)\n",
    "        indices = np.cumsum(n_values)\n",
    "\n",
    "        column_indices = (X_int + indices[:-1]).ravel()[mask]\n",
    "        row_indices = np.repeat(np.arange(n_samples, dtype=np.int32),\n",
    "                                n_features)[mask]\n",
    "        data = np.ones(n_samples * n_features)[mask]\n",
    "\n",
    "        out = sparse.csc_matrix((data, (row_indices, column_indices)),\n",
    "                                shape=(n_samples, indices[-1]),\n",
    "                                dtype=self.dtype).tocsr()\n",
    "        if self.encoding == 'onehot-dense':\n",
    "            return out.toarray()\n",
    "        else:\n",
    "            return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "encoder = CategoricalEncoder()\n",
    "housing_cat_reshaped = housing_cat.values.reshape(-1,1)\n",
    "housing_cat_1hot = encoder.fit_transform(housing_cat_reshaped)\n",
    "housing_cat_1hot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_cat_1hot.toarray()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 自定义转换器"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "尽管 Scikit-Learn 提供了许多有用的转换器，你还是需要自己动手写转换器执行任务，比如自定义的清理操作，或属性组合。你需要让自制的转换器与 Scikit-Learn 组件（比如流水线）无缝衔接工作，因为 Scikit-Learn 是依赖鸭子类型的（而不是继承），你所需要做的是创建一个类并执行三个方法：fit()（返回self），transform()，和fit_transform()。通过添加TransformerMixin作为基类，可以很容易地得到最后一个。另外，如果你添加BaseEstimator作为基类（且构造器中避免使用*args和**kargs），你就能得到两个额外的方法（get_params() 和set_params()），二者可以方便地进行超参数自动微调。例如，一个小转换器类添加了上面讨论的属性："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "rooms_ix, bedrooms_ix, population_ix, household_ix = 3, 4, 5, 6\n",
    "\n",
    "class CombinedAttributesAdder(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, add_bedrooms_per_room = True): # no *args or **kargs\n",
    "        self.add_bedrooms_per_room = add_bedrooms_per_room\n",
    "    def fit(self, X, y=None):\n",
    "        return self  # nothing else to do\n",
    "    def transform(self, X, y=None):\n",
    "        rooms_per_household = X[:, rooms_ix] / X[:, household_ix]\n",
    "        population_per_household = X[:, population_ix] / X[:, household_ix]\n",
    "        if self.add_bedrooms_per_room:\n",
    "            bedrooms_per_room = X[:, bedrooms_ix] / X[:, rooms_ix]\n",
    "            return np.c_[X, rooms_per_household, population_per_household,\n",
    "                         bedrooms_per_room]\n",
    "        else:\n",
    "            return np.c_[X, rooms_per_household, population_per_household]\n",
    "\n",
    "attr_adder = CombinedAttributesAdder(add_bedrooms_per_room=False)\n",
    "housing_extra_attribs = attr_adder.transform(housing.values)\n",
    "# housing_extra_attribs = pd.DataFrame(housing_extra_attribs)\n",
    "# housing_extra_attribs['ocean_proximity'] = housing_cat_encoded"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在这个例子中，转换器有一个超参数add_bedrooms_per_room，默认设为True（提供一个合理的默认值很有帮助）。这个超参数可以让你方便地发现添加了这个属性是否对机器学习算法有帮助。更一般地，你可以为每个不能完全确保的数据准备步骤添加一个超参数。数据准备步骤越自动化，可以自动化的操作组合就越多，越容易发现更好用的组合（并能节省大量时间）。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 特征缩放"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据要做的最重要的转换之一是特征缩放。除了个别情况，当输入的数值属性量度不同时，机器学习算法的性能都不会好。这个规律也适用于房产数据：总房间数分布范围是 6 到 39320，而收入中位数只分布在 0 到 15。注意通常情况下我们不需要对目标值进行缩放;<bar>\n",
    "    \n",
    "有两种常见的方法可以让所有的属性有相同的量度：线性函数归一化（Min-Max scaling）和标准化（standardization）。<bar>\n",
    "    \n",
    "线性函数归一化（许多人称其为归一化（normalization））很简单：值被转变、重新缩放，直到范围变成 0 到 1。我们通过减去最小值，然后再除以最大值与最小值的差值，来进行归一化。Scikit-Learn 提供了一个转换器MinMaxScaler来实现这个功能。它有一个超参数feature_range，可以让你改变范围，如果不希望范围是 0 到 1。<bar>\n",
    "    \n",
    "标准化就很不同：首先减去平均值（所以标准化值的平均值总是 0），然后除以方差，使得到的分布具有单位方差。与归一化不同，标准化不会限定值到某个特定的范围，这对某些算法可能构成问题（比如，神经网络常需要输入值得范围是 0 到 1）。但是，标准化受到异常值的影响很小。例如，假设一个街区的收入中位数由于某种错误变成了 100，归一化会将其它范围是 0 到 15 的值变为 0-0.15，但是标准化不会受什么影响。Scikit-Learn 提供了一个转换器StandardScaler来进行标准化。<bar>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 转换流水线"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "你已经看到，存在许多数据转换步骤，需要按一定的顺序执行。幸运的是，Scikit-Learn 提供了类Pipeline，来进行这一系列的转换。下面是一个数值属性的小流水线："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "num_pipeline = Pipeline([\n",
    "    ('imputer',SimpleImputer(strategy = 'median')),\n",
    "    ('attribs_adder',CombinedAttributesAdder()),\n",
    "    ('std_scaler',StandardScaler()),\n",
    "])\n",
    "\n",
    "housing_num_tr = num_pipeline.fit_transform(housing_num)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果不需要手动将 PandasDataFrame中的数值列转成 Numpy 数组的格式，而可以直接将DataFrame输入 pipeline 中进行处理就好了。Scikit-Learn 没有工具来处理 PandasDataFrame，因此我们需要写一个简单的自定义转换器来做这项工作："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "\n",
    "class DataFrameSelector(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self,attribute_names):\n",
    "        self.attribute_names = attribute_names\n",
    "    def fit(self,X,y=None):\n",
    "        return self\n",
    "    def transform(self,X):\n",
    "        return X[self.attribute_names].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "每个子流水线都以一个选择转换器开始：通过选择对应的属性（数值或分类）、丢弃其它的，来转换数据，并将输出DataFrame转变成一个 NumPy 数组。这样，你就可以很简单的写出一个以 PandasDataFrame为输入并且可以处理数值的流水线： 该流水线从DataFrameSelector开始获取数值属性，前面讨论过的其他数据处理步骤紧随其后。 并且你也可以通过使用DataFrameSelector选择类别属性并为其写另一个流水线然后应用LabelBinarizer.\n",
    "\n",
    "你现在就有了一个对数值的流水线，你还需要对分类值应用LabelBinarizer：如何将这些转换写成一个流水线呢？Scikit-Learn 提供了一个类FeatureUnion实现这个功能。你给它一列转换器（可以是所有的转换器），当调用它的transform()方法，每个转换器的transform()会被并行执行，等待输出，然后将输出合并起来，并返回结果（当然，调用它的fit()方法就会调用每个转换器的fit()）。一个完整的处理数值和类别属性的流水线如下所示："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import FeatureUnion\n",
    "num_attribs = list(housing_num)\n",
    "cat_attribs = [\"ocean_proximity\"]\n",
    "\n",
    "num_pipeline = Pipeline([\n",
    "    ('selector',DataFrameSelector(num_attribs)),\n",
    "    ('Simpleimputer', SimpleImputer(strategy=\"median\")),\n",
    "    ('attribs_adder', CombinedAttributesAdder()),\n",
    "    ('std_scaler', StandardScaler()),\n",
    "])\n",
    "\n",
    "cat_pipeline = Pipeline([\n",
    "        ('selector', DataFrameSelector(cat_attribs)),\n",
    "        ('cat_encoder', CategoricalEncoder(encoding=\"onehot-dense\")),\n",
    "    ])\n",
    "\n",
    "full_pipeline = FeatureUnion(transformer_list=[\n",
    "    ('num_pipeline',num_pipeline),\n",
    "    ('cat_pipeline',cat_pipeline),\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "你可以很简单地运行整个流水线："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_prepared = full_pipeline.fit_transform(housing)\n",
    "housing_prepared"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 选择并训练模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 在训练集上训练和评估"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "好消息是基于前面的工作，接下来要做的比你想的要简单许多。像前一章那样，我们先来训练一个线性回归模型："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "lr = LinearRegression()\n",
    "lr.fit(housing_prepared, housing_lables)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "让我们使用 Scikit-Learn 的mean_squared_error函数，用全部训练集来计算下这个回归模型的 RMSE："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "housing_predictions = lr.predict(housing_prepared)\n",
    "lr_mse = mean_squared_error(housing_lables, housing_predictions)\n",
    "lr_rmse = np.sqrt(lr_mse)\n",
    "lr_rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "大多数街区的median_housing_values位于 120000 到 265000 美元之间，因此预测误差 68628 美元不能让人满意。这是一个模型欠拟合训练数据的例子。当这种情况发生时，意味着特征没有提供足够多的信息来做出一个好的预测，或者模型并不强大。就像前一章看到的，修复欠拟合的主要方法是选择一个更强大的模型，给训练算法提供更好的特征，或去掉模型上的限制。这个模型还没有正则化，所以排除了最后一个选项。你可以尝试添加更多特征（比如，人口的对数值），但是首先让我们尝试一个更为复杂的模型，看看效果。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "训练一个DecisionTreeRegressor。这是一个强大的模型，可以发现数据中复杂的非线性关系（决策树会在第 6 章详细讲解）。代码看起来很熟悉："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "tree_reg = DecisionTreeRegressor()\n",
    "tree_reg.fit(housing_prepared, housing_lables)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_predictions = tree_reg.predict(housing_prepared)\n",
    "tree_mse = mean_squared_error(housing_lables, housing_predictions)\n",
    "tree_rmse = np.sqrt(tree_mse)\n",
    "tree_rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "没有误差？这个模型可能是绝对完美的吗？当然，更大可能性是这个模型严重过拟合数据。如何确定呢？如前所述，直到你准备运行一个具备足够信心的模型，都不要碰测试集，因此你需要使用训练集的部分数据来做训练，用一部分来做模型验证"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 使用交叉验证做更佳的评估"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "评估决策树模型的一种方法是用函数train_test_split来分割训练集，得到一个更小的训练集和一个验证集，然后用更小的训练集来训练模型，用验证集来评估。这需要一定工作量，并不难而且也可行。\n",
    "\n",
    "另一种更好的方法是使用 Scikit-Learn 的交叉验证功能。下面的代码采用了 K 折交叉验证（K-fold cross-validation）：它随机地将训练集分成十个不同的子集，成为“折”，然后训练评估决策树模型 10 次，每次选一个不用的折来做评估，用其它 9 个来做训练。结果是一个包含 10 个评分的数组："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "scores = cross_val_score(tree_reg, housing_prepared, housing_lables,\n",
    "                        scoring = \"neg_mean_squared_error\", cv =10)\n",
    "tree_rmse_scores = np.sqrt(-scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    " def display_scores(scores):\n",
    "        print(\"Scores:\", scores)\n",
    "        print(\"Mean:\", scores.mean())\n",
    "        print(\"Standard deviation:\", scores.std())\n",
    "display_scores(tree_rmse_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_scores = cross_val_score(lr, housing_prepared, housing_lables,\n",
    "                            scoring=\"neg_mean_squared_error\", cv=10)\n",
    "lr_rmse_scores = np.sqrt(-lr_scores)\n",
    "display_scores(lr_rmse_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "判断没错：决策树模型过拟合很严重，它的性能比线性回归模型还差。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "forest_reg = RandomForestRegressor()\n",
    "forest_reg.fit(housing_prepared, housing_lables)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_predictions = forest_reg.predict(housing_prepared)\n",
    "forest_mse = mean_squared_error(housing_lables, housing_predictions)\n",
    "forest_rmse = np.sqrt(forest_mse)\n",
    "forest_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = cross_val_score(forest_reg, housing_prepared, housing_lables,\n",
    "                        scoring = \"neg_mean_squared_error\", cv =10)\n",
    "forest_rmse_scores = np.sqrt(-scores)\n",
    "display_scores(forest_rmse_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "要保存每个试验过的模型，以便后续可以再用。要确保有超参数和训练参数，以及交叉验证评分，和实际的预测值。这可以让你比较不同类型模型的评分，还可以比较误差种类。你可以用 Python 的模块pickle，非常方便地保存 Scikit-Learn 模型，或使用sklearn.externals.joblib，后者序列化大 NumPy 数组更有效率："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "from sklearn.externals import joblib\n",
    "\n",
    "joblib.dump(my_model, \"my_model.pkl\")\n",
    "#### 然后\n",
    "my_model_loaded = joblib.load(\"my_model.pkl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 模型微调"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 网格搜索"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用 Scikit-Learn 的GridSearchCV来做这项搜索工作。你所需要做的是告诉GridSearchCV要试验有哪些超参数，要试验什么值，GridSearchCV就能用交叉验证试验所有可能超参数值的组合。例如，下面的代码搜索了RandomForestRegressor超参数值的最佳组合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "param_grid = [\n",
    "    {'n_estimators':[3,10,30], 'max_features':[2,4,6,8]},\n",
    "    {'bootstrap':[False],'n_estimators':[3,10],'max_features':[2,3,4]},\n",
    "]\n",
    "\n",
    "forest_reg = RandomForestRegressor()\n",
    "\n",
    "grid_search = GridSearchCV(forest_reg, param_grid, cv=5, scoring = 'neg_mean_squared_error')\n",
    "\n",
    "grid_search.fit(housing_prepared, housing_lables)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "param_grid告诉 Scikit-Learn 首先评估所有的列在第一个dict中的n_estimators和max_features的3 × 4 = 12种组合（不用担心这些超参数的含义，会在第 7 章中解释）。然后尝试第二个dict中超参数的2 × 3 = 6种组合，这次会将超参数bootstrap设为False而不是True（后者是该超参数的默认值）。\n",
    "\n",
    "网格搜索会探索12 + 6 = 18种RandomForestRegressor的超参数组合，会训练每个模型五次（因为用的是五折交叉验证）。换句话说，训练总共有18 × 5 = 90轮！K 折将要花费大量时间，完成后，你就能获得参数的最佳组合，如下所示："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果GridSearchCV是以（默认值）refit=True开始运行的，则一旦用交叉验证找到了最佳的估计器，就会在整个训练集上重新训练。这是一个好方法，因为用更多数据训练会提高性能"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cvres = grid_search.cv_results_\n",
    "for mean_score, params in zip(cvres[\"mean_test_score\"], cvres[\"params\"]):\n",
    "    print(np.sqrt(-mean_score), params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 随机搜索"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "当探索相对较少的组合时，就像前面的例子，网格搜索还可以。但是当超参数的搜索空间很大时，最好使用RandomizedSearchCV。这个类的使用方法和类GridSearchCV很相似，但它不是尝试所有可能的组合，而是通过选择每个超参数的一个随机值的特定数量的随机组合。这个方法有两个优点：\n",
    "\n",
    "如果你让随机搜索运行，比如 1000 次，它会探索每个超参数的 1000 个不同的值（而不是像网格搜索那样，只搜索每个超参数的几个值）。\n",
    "\n",
    "你可以方便地通过设定搜索次数，控制超参数搜索的计算量。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import randint\n",
    "\n",
    "param_distribs = {\n",
    "        'n_estimators': randint(low = 1, high = 200),\n",
    "        'max_features': randint(low = 1, high = 8),\n",
    "    }\n",
    "\n",
    "forest_reg = RandomForestRegressor(random_state = 42)\n",
    "rnd_search = RandomizedSearchCV(forest_reg, param_distributions = param_distribs,\n",
    "                                n_iter = 10, cv = 5, scoring = 'neg_mean_squared_error', random_state = 42)\n",
    "rnd_search.fit(housing_prepared, housing_lables)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for mean_score, params in zip(cvres[\"mean_test_score\"], cvres[\"params\"]):\n",
    "    print(np.sqrt(-mean_score), params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rnd_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rnd_search.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 集成方法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "另一种微调系统的方法是将表现最好的模型组合起来。组合（集成）之后的性能通常要比单独的模型要好（就像随机森林要比单独的决策树要好），特别是当单独模型的误差类型不同时。我们会在第 7 章更深入地讲解这点。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 分析最佳模型和它们的误差"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通过分析最佳模型，常常可以获得对问题更深的了解。比如，RandomForestRegressor可以指出每个属性对于做出准确预测的相对重要性："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_importances = grid_search.best_estimator_.feature_importances_\n",
    "feature_importances"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "将重要性分数和属性名放到一起："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "extra_attribs = [\"rooms_per_hhold\", \"pop_per_hhold\", \"bedrooms_per_room\"]\n",
    "# cat_one_hot_attribs = list(encoder.classes_)\n",
    "cat_encoder = full_pipeline.named_transformers_[\"cat\"]\n",
    "attributes = num_attribs + extra_attribs + cat_one_hot_attribs\n",
    "sorted(zip(feature_importances,attributes), reverse=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 用测试集评估系统"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "调节完系统之后，你终于有了一个性能足够好的系统。现在就可以用测试集评估最后的模型了。这个过程没有什么特殊的：从测试集得到预测值和标签，运行full_pipeline转换数据（调用transform()，而不是fit_transform()！），再用测试集评估最终模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_model = grid_search.best_estimator_\n",
    "\n",
    "y_test = strat_test_set[\"median_house_value\"].copy()\n",
    "X_test = strat_test_set.drop(\"median_house_value\", axis=1)\n",
    "\n",
    "X_test_prepared = full_pipeline.transform(X_test)\n",
    "final_predictions = final_model.predict(X_test_prepared)\n",
    "\n",
    "final_mse = mean_squared_error(y_test, final_predictions)\n",
    "final_rmse = np.sqrt(final_mse)   # => evaluates to 48,209.6\n",
    "final_rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "评估结果通常要比交叉验证的效果差一点，如果你之前做过很多超参数微调（因为你的系统在验证集上微调，得到了不错的性能，通常不会在未知的数据集上有同样好的效果）。这个例子不属于这种情况，但是当发生这种情况时，你一定要忍住不要调节超参数，使测试集的效果变好；这样的提升不能推广到新数据上。\n",
    "\n",
    "然后就是项目的预上线阶段：你需要展示你的方案（重点说明学到了什么、做了什么、没做什么、做过什么假设、系统的限制是什么，等等），记录下所有事情，用漂亮的图表和容易记住的表达（比如，“收入中位数是房价最重要的预测量”）做一次精彩的展示。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 启动、监控、维护系统"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "很好，你被允许启动系统了！你需要为实际生产做好准备，特别是接入输入数据源，并编写测试。\n",
    "\n",
    "你还需要编写监控代码，以固定间隔检测系统的实时表现，当发生下降时触发报警。这对于捕获突然的系统崩溃和性能下降十分重要。做监控很常见，是因为模型会随着数据的演化而性能下降，除非模型用新数据定期训练。\n",
    "\n",
    "评估系统的表现需要对预测值采样并进行评估。这通常需要人来分析。分析者可能是领域专家，或者是众包平台（比如 Amazon Mechanical Turk 或 CrowdFlower）的工人。不管采用哪种方法，你都需要将人工评估的流水线植入系统。\n",
    "\n",
    "你还要评估系统输入数据的质量。有时因为低质量的信号（比如失灵的传感器发送随机值，或另一个团队的输出停滞），系统的表现会逐渐变差，但可能需要一段时间，系统的表现才能下降到一定程度，触发警报。如果监测了系统的输入，你就可能尽量早的发现问题。对于线上学习系统，监测输入数据是非常重要的。\n",
    "\n",
    "最后，你可能想定期用新数据训练模型。你应该尽可能自动化这个过程。如果不这么做，非常有可能你需要每隔至少六个月更新模型，系统的表现就会产生严重波动。如果你的系统是一个线上学习系统，你需要定期保存系统状态快照，好能方便地回滚到之前的工作状态。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 实践！"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "望这一章能告诉你机器学习项目是什么样的，你能用学到的工具训练一个好系统。你已经看到，大部分的工作是数据准备步骤、搭建监测工具、建立人为评估的流水线和自动化定期模型训练，当然，最好能了解整个过程、熟悉三或四种算法，而不是在探索高级算法上浪费全部时间，导致在全局上的时间不够。\n",
    "\n",
    "因此，如果你还没这样做，现在最好拿起台电脑，选择一个感兴趣的数据集，将整个流程从头到尾完成一遍。一个不错的着手开始的地点是竞赛网站，比如 http://kaggle.com/：你会得到一个数据集，一个目标，以及分享经验的人。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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